Modeling Farmers' Response to Uncertain Rainfall in Burkina Faso: A Stochastic Programming Approach

Abstract

Farmers on the Central Plateau of Burkina Faso in West Africa cultivate under precarious conditions. Rainfall variability is extremely high in this area and accounts for much of the uncertainty surrounding the farmers' decision-making process. Strategies to cope with these risks are typically dynamic. Sequential decision making is one of the most important ways to cope with risk due to uncertain rainfall. In this paper, a stochastic programming model is presented to describe farmers' sequential decisions in reaction to rainfall. The model describes farmers' strategies of production, consumption, selling, purchasing, and storage from the start of the growing season until one year after the harvest period. This dynamic model better describes farmers' strategies than do static models that are usually applied. This study draws important policy conclusions regarding reorientation of research programs and illustrates how operations research techniques can be usefully applied to study grass root problems in developing countries.

Processes of adaptation in farm decision-making models. A review

“…In applied agricultural economics, stochastic production models are more and more commonly used to represent the sequential production decisions by farmers by specifying the production technology through a series of operational steps involving production inputs. These inputs have often the dual purpose of controlling crop yield or cattle output level on the one hand and controlling production risk on the other (Burt 1993;Maatman et al 2002;Ritten et al 2010). Furthermore, sequential production decisions with risk and uncertainty can also be specified in a dynamic framework to account for intertemporal substitutability between inputs (Fafchamps 1993).…”

“…They may purchase grain or sell livestock to obtain more income and cover household needs. To minimize deficits in various nutrients in an African household, Maatman et al (2002) built a model in which decisions about late sowing and weeding intensity are decided after observing a second rainfall in the cropping season.…”

Bio-economic modeling of wine grape protection strategies for environmental policy assessment

“…Since the publication of Rae's seminal papers (Rae, 1971a(Rae, , 1971b, Discrete Stochastic Programming or DSP has been widely used in the field of agricultural economics (Aplan and Hauer, 1993;Birge and Louveaux, 1997). One particular focus of this work has been farmers' response to climatic uncertainty (Cortignani, 2010;Kingwell et al, 1993;Maatman et al, 2002). The VINEPA model is the multi-periodic DSP model we created for this study.…”

Crop plan optimization under risk on a farm level in the Czech Republic

“…From the modelling perspective, the OR models in agriculture can be classified as deterministic and stochastic, according to the certainty of the value of the parameters used. Where the parameters are assumed to be deterministic, apart from the linear programming, also the dynamic programming, the mixed integer programming and the goal programming are frequently used, otherwise the stochastic modelling approaches are employed, these including mainly the stochastic programming, the stochastic dynamic programming, the simulation and risk programming (Maatman et al 2002;Lowe and Preckel 2004;Torkamani 2005;Benjamin et al 2009;Bohle et al 2009). …”